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public class LogisticRegression {

public int N;

public int n_in;

public int n_out;

public double[][] W;

public double[] b;

public LogisticRegression(int N, int n_in, int n_out) {

this.N = N;

this.n_in = n_in;

this.n_out = n_out;

W = new double[this.n_out][this.n_in];

b = new double[this.n_out];

}

public void train(int[] x, int[] y, double lr) {

double[] p_y_given_x = new double[n_out];

double[] dy = new double[n_out];

for(int i=0; i<n_out; i++) {

p_y_given_x[i] = 0;

for(int j=0; j<n_in; j++) {

p_y_given_x[i] += W[i][j] * x[j];

}

p_y_given_x[i] += b[i];

}

softmax(p_y_given_x);

for(int i=0; i<n_out; i++) {

dy[i] = y[i] - p_y_given_x[i];

for(int j=0; j<n_in; j++) {

W[i][j] += lr * dy[i] * x[j] / N;

}

b[i] += lr * dy[i] / N;

}

}

public void softmax(double[] x) {

double max = 0.0;

double sum = 0.0;

for(int i=0; i<n_out; i++) {

if(max < x[i]) {

max = x[i];

}

}

for(int i=0; i<n_out; i++) {

x[i] = Math.exp(x[i] - max);

sum += x[i];

}

for(int i=0; i<n_out; i++) {

x[i] /= sum;

}

}

public void predict(int[] x, double[] y) {

for(int i=0; i<n_out; i++) {

y[i] = 0;

for(int j=0; j<n_in; j++) {

y[i] += W[i][j] * x[j];

}

y[i] += b[i];

}

softmax(y);

}

}